Abstract
Digital Transformation (DT) has become an important issue for organisations. It is proven that DT fuels Digital Innovation in organisations. It is well-known that technologies and practices such as distributed ledger technologies, open source, analytics, big data, and artificial intelligence (AI) enhance DT. Among those technologies, AI provides tools to support decision-making and automatically decide. Cooperatives are organisations with a mutualistic scope and are characterised by having participatory cooperative governance due to the principle of democratic control by the members. In a context where DT is here to stay, where the dematerialisation of processes can bring significant advantages to any organisation, this article presents a critical reflection on the dangers of using AI technologies in cooperatives. We base this reflection on the Portuguese cooperative code. We emphasise that this code is not very different from the ones of other countries worldwide as they are all based on the Statement of Cooperative Identity defined by the International Cooperative Alliance. We understand that we cannot stop the entry of AI technologies into the cooperatives. Therefore, we present a framework for using AI technologies in cooperatives to avoid damaging the principles and values of this type of organisations.

Abstract
Agricultural cooperatives remain a significant component of the food and agriculture industry to help the stakeholders to provide services and have opportunities for themselves. One of the aims of an agricultural cooperative is to answer to the needs within the communities of the farmers. Agricultural cooperatives enable individual farmers to increase productivity and maximise their social welfare. Together the farmer members of an agricultural cooperative can buy input supplies cheaper and sell more of their products in larger markets at higher prices, which is not possible for an individual smallholder farmer otherwise. Some studies have shown that farmers who were members of cooperatives have gained higher revenue for their products and spent less on input. However, organising the hundreds of farmers into smaller groups to perform collective farming and marketing is crucial to strengthening their position in the food and agriculture industry. Thereby, in our work, we consider an agricultural cooperative of smallholder farmers as a multi-agent based coalitional model, where coalitions are formed based on the similarity among the smallholder farmers. In this paper, we propose a model and implement a heuristic-based algorithm to find the disjoint partition of the agents set. We evaluate the model and the algorithm based on the following criteria: (i) individual gain, (ii) runtime analysis, (iii) solution quality, and (iv) scalability. We theoretically prove that our coalitional model of an agricultural cooperative has conciseness, expressiveness and efficiency properties. Experimental results confirm that our algorithm is time efficient and scalable. We show, both empirically and theoretically, that our algorithm generates a solution within a bound of the optimal solution. We also show that our coalition model generates positive revenue for the smallholder farmers and the payoff division rule is individual rational. In addition, we generate a new dataset in the context of an agricultural cooperative to show the effectiveness and efficiency of the proposed coalitional model of the cooperative.

Abstract
O Centro de Estudos Organizacionais e Sociais do Politécnico do Porto (CEOS.PP) juntou-se à iniciativa anual da Comissão Europeia - "Noite Europeia dos Investigadores" – lançada em 2005 - com o Dia do Investigador CEOS [DICEOS23]. O objetivo deste evento, que decorreu no dia 29 de setembro de 2023, foi o de divulgar o trabalho desenvolvido pelos investigadores do CEOS.PP, um momento que contou com um conjunto de atividades para criar sinergias entre os investigadores deste centro de investigação e abrir caminhos para o futuro.

Abstract
The optimal Coalition Structure Generation (CSG) problem for a given set of agents finds a partition of the agent set that maximises social welfare. The CSG problem is an NP-hard optimisation problem, where the search space grows exponentially. The exact and approximation algorithms focus on finding an optimal solution or a solution within a known bound from the optimum. However, as the number of agents increases linearly, the search space increases exponentially and a practical option here is to use heuristic algorithms. Heuristic algorithms are suitable for solving the optimisation problems because of their less computational complexity. TACOS is a heuristic method for the CSG problem that finds high-quality solutions quickly using a neighbourhood search performed with a memory. However, some of the neighbourhood searches by TACOS can be performed simultaneously. Therefore, this paper proposes a parallel version of the TACOS algorithm (P-TACOS) for the CSG problem, intending to find a better solution than TACOS. We evaluated P-TACOS using eight (8) benchmark data distributions. Results show that P-TACOS achieves better results for all eight (8) data distributions. P-TACOS achieves the highest gain, 74.23%, for the Chisquare distribution and the lowest gain, 0.01%, for the Normal distribution. We also examine how often P-TACOS generates better results than TACOS. In the best case, it generates better results for 92.30% of the time (for the Rayleigh and Agent-based Normal distributions), and in the worst case, 38.46% of the time (for the Weibull distribution).

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